Abstract

The emerging technique of DGT (diffusive gradients in thin films) is shown to be capable of performing new speciation measurements in situ in natural waters. In DGT, metals are bound to a resin layer after passing through a well-defined diffusion layer. Cd was measured in solutions containing glycine, EDTA, and fulvic (FA) and humic acids (HA) by atomic absorption spectroscopy (AAS), anodic stripping voltammetry (ASV), and DGT. DGT measured similar labile fractions to ASV, with detailed differences being consistent with a thicker diffusion layer allowing more dissociation of labile complexes and a slower diffusion of FA and HA complexes through the gel. When single measurements are made in complex solutions with DGT, precise quantification is impossible due to uncertainties concerning the distribution of species with different diffusion coefficients. A new procedure was proposed based on the advantage of DGT of being able to control the pore size of the diffusive gel layer. Small (inorganic) species diffuse freely through all gels but larger FA and HA (organic) complexes diffuse less freely in more constrained gels. When measurements were made on known solutions of Cu and FA or HA, it was possible to quantify the inorganic and organic species separately. They agreed well with predictions made using the WHAM speciation code. Multiple DGT units were also deployed in situ in a stream with high dissolved organic carbon (14.6 mg/L). The systematic differences between the devices with different gel compositions enabled determination, for the first time, of the in situ concentrations of both labile inorganic and organic species in natural water. A single DGT device with a constrained gel can be used to quantify inorganic species directly, providing absolute accuracy is not required. This ability of DGT to measure well-defined fractions of metals in situ using a simple device gives it considerable potential as a regulatory tool. A direct speciation measurement may be preferable to modeling approaches which require diverse input data that are difficult to determine.